Increasing student science, technology, engineering and mathematics engagement through phyphox activities: Three practical examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract For decades, educators in science, technology, engineering and mathematics (STEM) have strived to break the vicious circle of student disengagement at both secondary and post‐secondary levels. Despite the widespread availability of technologies like smartphones, STEM pedagogies have largely remained unchanged. Too often, students learn STEM theoretically with little hands‐on experience or opportunities to engage in authentic, research‐like activities. Modern smartphones can offer unprecedented opportunities for active STEM learning, but can also serve as distractors. Therefore, it is essential for teachers to acquire the pedagogical knowledge to harness these powerful tools effectively. This paper explores the potential of integrating smartphones into physics labs to enrich STEM learning. By leveraging smartphones' advanced capabilities for experimental design, data collection, and analysis, we have implemented a smartphone‐enhanced pedagogical approach in secondary physics classes and province‐wide Physics Olympics. We also implemented smartphone‐enhanced STEM pedagogies in teacher education. Our initial pilot study has yielded promising outcomes: enhanced student engagement in physics and deeper conceptual understanding. To advance this initiative, we propose structured teacher mentorship and professional development, empowering STEM educators to seamlessly integrate smartphones into their teaching. By embracing these modern educational tools, adopting evidence‐based pedagogical approaches, and supporting future and practicing educators we can make STEM learning more engaging and relevant for all students.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it